Course Overview

This course explores a variety of modern techniques for learning to sample from an unknown probability distribution given examples. Generative models are an active area of research: most of the techniques we discuss in this course have been developed in the last 10 years. This course is integrated tightly with the current research literature, and will provide the context needed to read papers on the most recent developments in the field. The lectures will focus on the theoretical and mathematical foundations of generative modeling techniques. The homeworks will consist a mix of analytical and computational exercises. The course project is intended to offer an opportunity to apply these ideas to your own research, or to more deeply investigate one of the topics discussed in the course.

Prerequisites: This course builds upon fundamental concepts in machine learning, as presented in e.g. CSE 546.

List of topics:

Course material covering similar topics from other institutions:

Discussion Forum and Email Communication

Discussion will take place on Ed. For private or confidential questions email the instructor. You may also get messages to the instructor through anonymous course feedback.


There will be 3 homeworks (each worth 20%) and a project (worth 40%).